A strong tracking particle filter for state estimation
Xiaolong Deng, Jinjun Lü, Rui Yue, Jianlin Zhang · 2011
One of the algorithmic cores of particle filter (PF) is the proposal distribution. A new proposal distribution combining the unscented Kalman filter (UKF) with strong tracking filter (STF) is presented. The scaling factor is added and is acquired by the techniques in the STF. It can be tuned to make the algorithm reliable and adaptive. In the nonlinear state estimation experiments, the results confirm the efficiency of the improved PF algorithm.